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Cheng Niu

ACL 2026×2AAAI 2026ICML 2026KDD 2026多模态强化学习与后训练推理可解释性效率与系统
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0 一作 · 5 篇 · ACL 2026 / AAAI 2026 / ICML 2026 / KDD 2026

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2026 顶会论文

5
作者位次 #3/5ACL 2026Poster· Natural Language Processing

Contextual Relevance and Adaptive Sampling for LLM -Based Document Reranking

Jerry Huang · Siddarth Madala · Cheng Niu · Julia Hockenmaier · Tong Zhang

摘要

Reranking algorithms have made progress in improving document retrieval quality by efficiently aggregating relevance judgments generated by large language models (LLMs). However, identifying relevant documents for queries that require in-depth reasoning remains a major challenge. Reasoning-intens...

推理可解释性效率与系统OpenReview 项目页
作者位次 #6/6ACL 2026Findings· Findings of ACL

O pen G en A lign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation

Hanning Zhang · Juntong Song · Juno Zhu · Yuanhao Wu · Tong Zhang · Cheng Niu

摘要

Reward Modeling is critical in evaluating and improving the generation of Large Language Models (LLMs). % They are typically trained on high-quality annotated datasets to distinguish between better and worse responses. While numerous recent works have shown its feasibility in improving safety, he...

对齐与偏好安全强化学习与后训练OpenReview 项目页

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